cs.CVMay 11, 2026

Quantifying Rodda and Graham Gait Classification from 3D Markerless Kinematics derived from a Single-view Video in a Heterogeneous Pediatric Clinical Cohort

Authors: Lauhitya ReddySeth DonahueJeremy BauerSusan SienkoAnita BagleyJoseph KrzakMaura EveldKaren Kruger+3 more

Organizations: Department of Biomedical Informatics, Emory University, Atlanta, GA, USA · Shriners Children’s, USA · The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA

Abstract

Cerebral Palsy (CP) is a neurological disorder of movement and the most common cause of lifelong physical disability in childhood. Approximately 75% of children with CP are ambulatory, and accurate gait assessment is central to preserving walking function, which deteriorates by mid-adulthood in a quarter to half of adults with CP. The Rodda and Graham classification system quantifies sagittal-plane gait deviations using ankle and knee z-scores derived from 3D Instrumented Gait Analysis (3D-IGA), but 3D-IGA is expensive and limited to specialized centers, while observational assessment shows only moderate inter-rater agreement. We developed a markerless gait analysis pipeline that quantifies Rodda and Graham knee and ankle z-scores directly from single-view clinical gait videos. Across 1,058 bilateral limb samples from 529 trials of 152 children (88 male, 63 female; age 12.1 ±\pm 4.0 years; 60 distinct primary diagnoses, cerebral palsy the most common at n=54n=54), the sagittal-view model achieved R2=0.80±0.02R^2 = 0.80 \pm 0.02 and CCC =0.89±0.02= 0.89 \pm 0.02 for knee z-scores and R2=0.57±0.02R^2 = 0.57 \pm 0.02 and CCC =0.72±0.02= 0.72 \pm 0.02 for ankle z-scores against 3D-IGA. Binary screening for excess knee flexion achieves AUROC =0.88= 0.88, correctly identifying 83% of affected children, and applying Rodda and Graham rules yields 43±143 \pm 1% 7-class accuracy with macro-AUROC =0.78±0.01= 0.78 \pm 0.01, ankle prediction error remaining the primary bottleneck. Beyond cross-sectional screening, continuous z-scores support longitudinal trajectory tracking across visits, providing a quantitative substrate for monitoring disease progression and treatment response unavailable from observational scales. These results demonstrate the feasibility of video-based z-score estimation, excess-flexion screening, and longitudinal trajectory tracking as a path toward scalable, objective gait assessment in low-resource clinical settings.

Explore similar work

CardsList
  1. Decoding Children's Gait Behavior

    Aug 1, 2026Yifan Shen, Boyi Li, Meihuan Huang +12Synthetic Motion DatasetsChildren